The impact of connection topology and agent size on cooperation in the iterated prisoner's dilemma
Bibliographic record
Abstract
This study revisits earlier work, concerning the evolutionary trajectory of agents trained to play iterated prisoner's dilemma on a combinatorial graph. The impact of different connection topologies, used to mediate both the play of prisoner's dilemma and the flow of genes during selection and replacement, is examined. The variety of connection topologies, stored as combinatorial graphs, is revisited and the analysis tools used are substantially improved. A novel tool called the play profile summarizes the distribution of behaviors over multiple replicates of the basic evolutionary algorithm and through multiple evolutionary epochs. The impact of changing the number of states used to encode agents is also examined. Changing the combinatorial graph on which the population resides is found to yield statistically significant differences in the play profiles. Changing the number of states in agents is also found to produce statistically significant differences in behavior. The use of multiple epochs in analysis of agent behavior demonstrates that the distribution of behaviors changes substantially over the course of evolution. The most common pattern is for agents to move toward the cooperative state over time, but this pattern is not universal. Another clear trend is that agents implemented with more states are less cooperative.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".